Here’s your roadmap for the year!
Every class session has four important sections. You should read about the details for each using the main menu at the top of this webpage.
Week Overview ( ): This page overviews the weeks
Lectures ( ): This page is for lectures
Labs ( ): This page is about labs
Extras ( ): This page is about Extras
Coursework ( ): This page contains the instructions for either the session exercise (1–3 brief tasks), or for the two mini projects and final project. Assignments are due by 11:59 PM on the day they’re listed.
Mini-Diss ( ): This page contains an interactive lesson that teaches you the principles and code you need to know. Go through these after doing the content.
tl;dr : You should follow this general process each week:
Check out the overview ( ) page and do any thinking/reading/prep suggested if you have the chance
Come to the lecture ( ) page
Attend the lab ( ) and get cracking
Use the plentiful Mini-Diss resources ( ) to help you smash the MD
https://littlemonkeylab.com/researchmethods/weeks/week02.html
Weekly schedule
week
date
what
topic
prepare
slides
lab
reading
minidiss
notes
1
Monday , 02 October
Lecture01
No lab
Tuesday , 03 October
Lab01
Welcome to STA 199
Here is some deadline information!
2
Monday , 09 October
Lecture02
Hello R!
Tuesday , 10 October
Lab02
Grammar of graphics
And something spicy here too!
3
Monday , 16 October
Lecture03
Visualizing various types of data
Tuesday , 17 October
Lab03
4
Monday , 23 October
Lecture04
Tuesday , 24 October
Lab04
5
Monday , 30 October
Lecture05
Tuesday , 31 October
Lab05
Reading Week
6
Monday , 13 November
Lecture06
Data visualization
Tuesday , 14 November
Lab06
Grammar of data wrangling
7
Monday , 20 November
Lecture07
Working with multiple data frames
Tuesday , 21 November
Lab07
8
Monday , 27 November
Lecture08
Data wrangling
Tuesday , 28 November
Lab08
Tidying data
9
Monday , 04 December
Lecture09
Data types and classes
Tuesday , 05 December
Lab09
10
Monday , 11 December
Lecture10
Data tidying
Tuesday , 12 December
Lab10
Importing and recoding data
Winter Break
11
Monday , 08 January
Lecture11
Tuesday , 09 January
Lab11
No lab - Work on Eam 1
12
Monday , 15 January
Lecture12
Data science ethics - Misrepresentation
Tuesday , 16 January
Lab12
Data science ethics - Algorithmic bias + data privacy
13
Monday , 22 January
Lecture13
No lab - Fall break
Tuesday , 23 January
Lab13
No Lec - Fall break
14
Monday , 29 January
Lecture14
Web scraping
Tuesday , 30 January
Lab14
Work on project proposal
15
Monday , 05 February
Lecture15
Functions + iteration
Tuesday , 06 February
Lab15
The language of models
Reading Week
16
Monday , 19 February
Lecture16
Probability + Simpson's Parado
Tuesday , 20 February
Lab16
Models with a single predictor
17
Monday , 26 February
Lecture17
Models with multiple predictors
Tuesday , 27 February
Lab17
18
Monday , 04 March
Lecture18
Predicting a numerical outcome
Tuesday , 05 March
Lab18
Models with multiple predictors + Overfitting
19
Monday , 11 March
Lecture19
Tuesday , 12 March
Lab19
20
Monday , 18 March
Lecture20
Tuesday , 19 March
Lab20